{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "195adbec-3d5d-4c4e-9887-f0022c72152f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in d:\\programdata\\anaconda3\\lib\\site-packages (1.26.4)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "d9d69aaa-b0ae-4de0-82a5-7df436d8c66d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AxcXFeO655xq9nq+vL4qKipq8xrJly+Dn52d+BDvJPiFERERt4qQztACZAo9KpYK7u3u91zw8PFBZWdniuRMmTEBaWhpUKhWeeuqpRq/X0rUSExOh1WrNj8LCwiu8EyIiIidhNNatwcMWHuvw9/dHSUlJvdfKysrg5ubW4rkqlQpXX301Vq9ejQ0bNjR6vZau5e7uDl9f33oPIiKiDu3ECUCnAzw9gfBwuauxOlkCT3R0NPbt22d+XlBQAL1eD39//ybP2bp1K1auXGl+rlKpoFQqG71eVlYWAgMDbVA5ERGRkzJ1Z40cCahU8tZiA7IEnrFjx0Kr1WLTpk0AgOXLlyMuLg5KpRI6nQ41NTUNzhk4cCCef/557NixAwUFBfjHP/6B6dOnAwASEhKwd+9epKeno7a2FsnJyZg4cWK73hMREZFDc9IFB01kG8Ozdu1azJkzBz169MD27duxfPlyAGLG1pdfftngnJEjR2LNmjV44oknMGLECPTp08e8wGDXrl2xcuVKTJw4EQEBAcjJycHixYvb9Z6IiIgcmhPP0AIAhWTJEsc2UlRUhMzMTMTGxqJbt25tvl5+fj7y8vJw7bXXtmpcjk6ng5+fH7RaLcfzEBFRx1NdDfj4iF9/+QXo10/uiizSmu9vWTvpAgMDrTrWJiwsDGFhYVa7HhERUYdw5IgIO126AJct8+JMHG7zUCIiIrKyy9ff+XMRX2fDwENERNTROfn4HYCBh4iIiEwztJxwhWUTBh4iIqKOTKcD8vLEMQMPEREROaVDhwBJAvr0AXr0kLsam2HgISIi6sg6QHcWwMBDRETUsXWAAcsAAw8REVHHxsBDRERETq24GCgsFGvvjBwpdzU2xcBDRETUUZnG7wweLLaWcGIMPERERB1VB+nOAhh4iIiIOq7Lt5Rwcgw8REREHZEk1XVpsYWHiIiInNIvvwAaDeDuDgwdKnc1NsfAQ0RE1BGZurOGDwfc3GQtpT0w8BAREXVEHag7C2DgISIi6pg60AwtgIGHiIio46mpAQ4fFscdYIYWwMBDRETU8Rw7BlRVAX5+QP/+clfTLhh4iIiIOprL199x6RhRoGPcJREREdXpYON3AAYeIiKijsc0Q6uDjN8BGHiIiIg6looKICdHHLOFh4iIiJzS4cOA0QgEBgK9esldTbth4CEiIupIOmB3FsDAQ0RE1LF0wAHLAAMPERFRx8LA07KsrKwW31NTU4O77777SushIiIiWyktBU6fFseRkfLW0s5UrXnzlClTcPLkSZw/fx6SJMGlkcWKjEYjMjIyrFYgERERWYlp/M6AAYBaLWsp7a1VgcfNzQ2urq6YNm0aysvLcfr0aYSEhKCgoAB9+/Y1/9pYECIiIiKZddDuLOAKx/AoFArk5eUhODgYeXl5CAsLq/erJEnWrpOIiIjaqoPO0AIsDDx6vR5///vfceHCBTz33HMoLi4GIIJPY78SERGRnZEktvC0xGAwYMyYMVCpVCguLkZFRYWt6yIiIiJrOnMGKCkBXF2BYcPkrqbdWRR4OnXqhJdffhn+/v5Yv349+vXrhxMnTqCmpgYnTpxAdXV1vV/Z0kNERGRnTN1ZERGAh4e8tcigVYOWDQYDAKBLly4YP348XFxcMHHiRACo9ysDDxERkZ3pwN1ZQCsCT3FxMW688UYYDAbs2rWryfcZjUb06dPHKsURERGRlTDwtOyPP/5A//79MWPGDBw/fhw9e/Zs8r2XLl3C3LlzrVYgERERtZHBABw6JI474AwtwMLAo1arsXv3brz//vuIioqCXq+Hu7s7PDw8GkxBNxgMqKqqQmJiok0KJiIiolbKywMqKgBvb2DgQLmrkYXF6/BcddVVePPNN3HixAnMmjUL7u7uWLZsGTQaTb2HTqdDdXW1LWsmIiKi1jB1Z0VFAUqlvLXIpNULDwYFBWHz5s3497//jbS0NJSXl1/RB+fk5CA6OhqdO3fGokWLLFqscO3atQgICICrqysmTJiAX3/91fyz+Ph4KBQK8yMuLu6K6iIiInI6psDTQbuzgDbslp6QkIDPPvsM3t7erT5Xr9cjPj4ekZGRyMzMRG5uLjZu3NjsOXv37kVSUhI2b96M06dPo6qqCgsXLjT//NChQzh69Ki5pSk1NbXVdRERETkl05T0DjpgGWhl4DEajfjmm2/Mz2tqajBt2rR675EkCa+++mqz10lLS4NWq0VKSgpCQ0OxdOlSrF+/vtlzjh8/jjVr1iAuLg5BQUG49957kZmZCQA4d+4cJEnCkCFDoFaroVar4eXl1ZpbIyIick6XLgFHjohjBh7LSJKEe+65B7W1tViwYAFcXV3NoWPfvn0AxPYSL7zwQrPXyc7ORkxMDDp16gQAiIiIQG5ubrPn3H///Zg6dar5+fHjxxEWFgYAOHDgAAwGA4KCguDl5YUZM2ZAo9E0eS29Xg+dTlfvQURE5JSysoDaWqB7dyA4WO5qZNOqwKNUKqFSiYldn376qbjAnzujT5482fw+T0/PZq+j0+kQEhJifq5QKKBUKpsNKZe7ePEi3nnnHfP09xMnTiAyMhJfffUVMjMzUVBQgGeffbbJ85ctWwY/Pz/zI7gD/wUgIiInd3l3VgdeGNjiwPPcc88hNzcXCoUCKpUKyr+M8vbz8zMft7TSskqlgru7e73XPDw8UFlZaVEtc+fORWxsLCZNmgQAeOaZZ5CWlobw8HAMGjQIK1aswPbt25s8PzExEVqt1vwoLCy06HOJiIgcTgdfcNDEonV4jEYjysrKMG7cOHM3VGNOnz4No9HY4owrf39/5OTk1HutrKwMbm5uLdby3nvv4fvvv0dWVlaT71Gr1SgtLTWvF/RX7u7ujb5ORETkdBh4AFjYwuPi4oLVq1fjxIkTTb6nrKwMkZGRiIqKanEdnujoaPOYHwAoKCiAXq+Hv79/s+cdOHAA8+fPx7Zt29CjRw/z69OmTat3vYMHD6Jnz54MNURE1LFpNMDJk+I4KkreWmTWqjE8vr6+KC4uxqOPPgq9Xo+Kiop6P/v999+h0WhaDBpjx46FVqvFpk2bAADLly9HXFwclEoldDodampqGpzz22+/IT4+Hk8//TQiIyNRXl5uXgMoIiICCxYswP79+7Fz504kJSVxewsiIqI/JxYhNBTo0kXeWmRmceCpqKjAN998A29vb3Tr1g0lJSUICgpCZWUlKioqUFZWZvGHqlQqrF27FnPmzEGPHj2wfft2LF++HIAIL19++WWDcz744ANcuHABixcvho+Pj/kBiDE5gwcPxvjx4zF//nw8/PDD3NqCiIiI3VlmCsmSJY4BREZGIiAgALm5uTh16hRCQkKQkZGBsLAwhIWFwdfX19yt1L17d1y4cKHFaxYVFSEzMxOxsbHo1q1b2+6kDXQ6Hfz8/KDVauHr6ytbHURERFY1ZQqQmgqkpAALFshdjdW15vvb4hae7du3Y+fOnebnCoUCvXv3RmBgIF5//XX8+uuvyMjIAACLW3sCAwNx8803yxp2iIiInJIkAfv3i2O28FgeeEzr5hiNRtTW1qKqqgqACD7jx4/HJ598gttuuw379+/Hhx9+aJtqiYiIyDJFRUBxsdgsdMQIuauRXasGLVdXV8PLywsqlQrp6enm8AMAUVFReP3115GQkIChQ4fapFgiIiKykGnBwSFDgGaWlOkoLFqHx8TNzQ3Hjh0DAAwYMAAGgwGPPPKI+ee33XYbsrKyml2rh4iIiNoBByzX06rA81dKpRJPP/10vdeWLVvWpoKIiIjIChh46mlVl9Zf7d69GwDqDTo+cOAAZs+e3baqiIiI6MoZjXVr8ERHy1uLnbC4hWfp0qXmBQV9fHzw0EMP4ZFHHsHJkyfNG4oCwLZt2/D7779bv1IiIiKyzIkTgE4HeHoC4eFyV2MXLG7hee2112AwGFBbW4t//etfAOp2Rff29gYAZGVlYcOGDXjppZdsUCoRERFZxNSdFRkJqNo0esVpWBx41Go1nnrqqXpjdi5v2dmxYwcmTZqE1atXY+DAgdatkoiIiCxnmqHF7iwzi2OfQqFo8NrZs2dx880349y5c3jqqaewfft2XHXVVVYtkIiIiFqJA5YbsLiFp7EdKNRqNW6++WZ4eHhAo9Fgw4YN0Ov1Vi2QiIiIWqG6GsjKEscMPGatmqV16dKlejuke3t747777kPXrl2Rn5+Ps2fPYtKkSTAajVYvlIiIiCxw5IgIPV26AH/ukkCtCDxarRaDBg1CeHg4uvy5xbwp/FRXV0OtViM1NRU6nQ4pKSm2qZaIiIiaZ+rOio4GGhmO0lFZPIansd3Pn3vuOQCAr68vzpw5gwsXLiA5ORnTp0/HggULoFQqrVcpERERtYzjdxplcQtPcXEx7rnnHly8eBGSJKGyshKzZs1CdXU1Dh06hD179uDmm2+GWq3Gzp07GXaIiIjkwBlajbKohefChQu47rrrMGTIEHh7eyM3NxdDhw6tN3PLNKh57NixyM3NtU21RERE1DSdDsjLE8cMPPVY1MJTWFiIW265BR9//LF5teXx48fDYDCYH0ajEXq9Hjt37kSvXr1sWjQRERE14tAhQJKAPn2AHj3krsauWNTCExkZicjISPNzHx8fTJo0qcH7XF1dcfXVV1uvOiIiIrIcu7OadEWbh/bu3RuPPfaYtWshIiKituCA5SZZ1MIjSRJuv/12eHh4WHTRESNGYN68eW0qjIiIiFqJgadJFgUehUKB0aNHw93dvdEtJi5XVVWFp556ClOmTEFwcLBViiQiIqIWFBcDhYWAi4vYNJTqsXgdnscffxxHjx7Fa6+9Vm/TUAAwGAyoqanBK6+8gp49e0Kj0ZgHNxMREVE7MI3fGTQI8PaWtxY71Ko94/39/XH11VdDpVLV21tLkiTU1taiU6dOAIAnn3wSPj4+1q2UiIiImsburGZZHHh+/vlnvPrqq1Aqleawo1AozK07a9euRXl5OSZPngyVSoXPPvvMVjUTERHRXzHwNMviwPPbb7/hl19+wb333tvoOB6NRoMxY8Zg7NixWLVqlTVrJCIiouZIEqekt6BVXVp/+9vfEBMTg6ioKKjVanh6eqJLly4IDAxEUVERnnjiCTz66KO2qpWIiIga88svgEYDuLsDQ4fKXY1dalXgAYA+ffrgp59+gqenJyRJQmlpKU6ePIldu3bh448/xr59+5CcnIyePXvaol4iIiL6K1N31ogRgJubvLXYKYsDj0KhwNatWxuMzTEYDKiqqsLPP/+MpUuXYuHChYiMjERmZiYCAgKsXS8RERH9FbuzWmRx4Bk7dix27tyJ6667DgBgNBrh4iIWat68eTPUajV8fHywefNmbN68mWGHiIiovXDAcoss3lpi586duOWWW1BcXAwAeOyxx/DUU0+huLgYa9euRd++ffHiiy/ijz/+wJ133mmzgomIiOgyNTXA4cPimIGnSRYHnnXr1mH9+vXo2bMnNm/ejLS0NDz55JPo2bMn9uzZg2+//Ra5ubkICgrC0qVLbVkzERERmRw7BlRVAX5+QFiY3NXYLYu7tFJTU83HkydPxujRo9Hjsq3nhw0bhm3btuGrr74yL0BIRERENmbqzoqOFttKUKMsCjzV1dUYPnw4cnNzAQCffPIJysrKsGvXLgBiPE9VVRVmz56NYcOGITY2Fnv27EFgYKDtKiciIiKO37GQRYHHzc0Nrq6u5uevv/46Jk+e3OB9er0eM2bMwNNPP82wQ0RE1B44Q8siFndpKZVK87FCocCSJUvwyy+/IDQ0FJ6engCAHTt2oE+fPpg9e7b1KyUiIqL6KiqAnBxxzBaeZrVqL63rr78eYWFh+O2333Dq1ClMmDABer0e7u7uCAkJwQ033IDVq1fbsl4iIiIyOXwYMBqBwECgVy+5q7FrFo9uCgwMxMKFCxEdHQ2lUonw8HCcP38eFy9exM8//4yXXnoJx44dw+jRo3Hx4kVb1kxEREQAu7NawaIWHqPRCDc3N9x4440AxBiecePGNXhfcnIyPvnkE9xxxx1IS0uzbqVERERUHwcsW8yiwFNbW4sRI0aYn7/77ruora3F+fPn0adPHxiNRuj1evTr1w8vvfQSIiMjsW/fPsTExNiscCIiog6PgcdiFnVpubm5ISgoCD///DMAYPTo0RgzZgxuu+02zJo1C76+vrj22muhVqvh4uKC9evXY/To0c1eMycnB9HR0ejcuTMWLVoESZJarGPt2rUICAiAq6srJkyYgF9//dX8s4yMDAwaNAhdu3ZFSkqKJbdFRETkuEpLgdOnxXFkpLy1OACLx/B89NFHuO666/DOO++gtrYWFRUVCA8Px65du9C7d+967x05ciQUCkWT19Lr9YiPjzdvMpqbm4uNGzc2+/l79+5FUlISNm/ejNOnT6OqqgoLFy4EAJSUlCAhIQEzZ87Ejz/+iC1btiA9Pd3SWyMiInI8pvE7AwYAarWspTgCiwOPn58f9u/fj++++w69evXCiy++iPz8fEydOhWxsbGIiIhAREQERo4ciTVr1jR7rbS0NGi1WqSkpCA0NBRLly7F+vXrmz3n+PHjWLNmDeLi4hAUFIR7770XmZmZAIAtW7YgICAASUlJ6N+/P5YsWdLi9YiIiBwau7NaxeJp6Xq9Hn369EG/fv1QWVmJf/7zn9ixYwc+/vjjeu87c+YMHnroITz88MNNXis7OxsxMTHmLSgiIiLMqzg35f7776/3/Pjx4wj7c8+Q7OxsjBs3ztyqNGrUKCQmJjZ7L3q93vxcp9M1+9lERER2hzO0WsXiwGMKBUuWLAEgZm4BwIABA+q9LyAgAHfddVeL1woJCTE/VygUUCqV0Gg06Ny5c4u1XLx4Ee+88w7ef/998/UGDx5s/rmvry+KioqaPH/ZsmV44YUXWvwcIiIiuyRJbOFpJYu7tM6dOwcAcHd3h7u7O1QqFT744IMG7/P19cWLL77Y7LVUKhXc3d3rvebh4YHKykqLapk7dy5iY2MxadKkRq/X0rUSExOh1WrNj8LCQos+l4iIyC6cOQOUlACursCwYXJX4xAsbuH5KxcXF0Re4ahwf39/5JiWwv5TWVkZ3NzcWjz3vffew/fff4+srKx61yspKbH4WqbQRkRE5JBM3VnDhgEeHvLW4iBk2Uc+Ojoa+/btMz8vKCiAXq+Hv79/s+cdOHAA8+fPx7Zt29CjR48mr5eVlcXNS4mIyHmZurM4fsdisgSesWPHQqvVYtOmTQCA5cuXIy4uDkqlEjqdDjU1NQ3O+e233xAfH4+nn34akZGRKC8vR3l5OQAgISEBe/fuRXp6Ompra5GcnIyJEye26z0RERG1G47faTWFZMmKfzbw2WefYdasWfDx8YHBYEBGRgbCw8PRt29frFq1ClOmTKn3/lWrVmHBggUNrmMq/6233sL8+fPh5+cHLy8v7N+/v14rUHN0Oh38/Pyg1Wrh6+vb5nsjIiKyGYMB8PMTO6UfOwZcNmmno2nN97dsgQcAioqKkJmZidjYWHTr1q3N18vPz0deXh6uvfbaVgUXBh4iInIYOTnA0KGAtzfwxx+AUil3RbJpzff3FQ9atobAwECrjrUJCwszr81DRETklEzdWVFRHTrstJYsY3iIiIjoCnH8zhVh4CEiInIkXGH5ijDwEBEROYpLl4AjR8QxW3hahYGHiIjIUWRlAbW1QI8eQHCw3NU4FAYeIiIiR3F5d9afG2aTZRh4iIiIHAUHLF8xBh4iIiJHwcBzxRh4iIiIHIFGA5w8KY6jouStxQEx8BARETmCzEzxa2go0KWLvLU4IAYeIiIiR8DurDZh4CEiInIEXHCwTRh4iIiI7J0kAfv3i2O28FwRBh4iIiJ7V1QEFBeLzUJHjJC7GofEwENERGTvTN1ZQ4YAnTrJW4uDYuAhIiKydxyw3GYMPERERPaOgafNGHiIiIjsmdFYtwYPZ2hdMQYeIiIie3biBKDTAZ6eQHi43NU4LAYeIiIie2bqzoqMBFQqeWtxYAw8RERE9sw0Q4vjd9qEgYeIiMiemVp4OH6nTRh4iIiI7FV1NZCVJY7ZwtMmDDxERET26sgREXq6dAFCQuSuxqEx8BAREdmry7uzFAp5a3FwDDxERET2igsOWg0DDxERkb1i4LEaBh4iIiJ7pNMBP/8sjjlDq80YeIiIiOzRoUOAJAF9+gDdu8tdjcNj4CEiIrJH7M6yKgYeIiIie2RaYZndWVbBwENERGSP2MJjVQw8RERE9qa4GCgsBFxcxKah1GYMPERERPbG1J01aBDg7S1vLU6CgYeIiMjesDvL6hh4iIiI7A0Dj9Ux8BAREdkTSeIMLRtg4CEiIrInv/wCaDSAuzswdKjc1TgNBh4iIiJ7YurOGjECcHOTtxYnIlvgycnJQXR0NDp37oxFixZBkiSLzsvPz4e/v3+D1+Pj46FQKMyPuLg4a5dMRERke+zOsglZAo9er0d8fDwiIyORmZmJ3NxcbNy4scXzTp8+jUmTJkGj0TT42aFDh3D06FFoNBpoNBqkpqbaoHIiIiIb44Blm5Al8KSlpUGr1SIlJQWhoaFYunQp1q9f3+J5kyZNwgMPPNDg9XPnzkGSJAwZMgRqtRpqtRpeXl62KJ2IiMh2amqAw4fFMQOPVckSeLKzsxETE4NOnToBACIiIpCbm9vieTt37sT06dMbvH7gwAEYDAYEBQXBy8sLM2bMaLQVyESv10On09V7EBERye7YMaCqCvDzA8LC5K7GqcgSeHQ6HUJCQszPFQoFlEplsyEFAPr169fo6ydOnEBkZCS++uorZGZmoqCgAM8++2yT11m2bBn8/PzMj+Dg4Cu7ESIiImsydWdFR4ttJchqZPndVKlUcHd3r/eah4cHKisrr+h6zzzzDNLS0hAeHo5BgwZhxYoV2L59e5PvT0xMhFarNT8KCwuv6HOJiIisiuN3bEYlx4f6+/sjJyen3mtlZWVws9L0O7VajdLSUuj1+gbBCgDc3d0bfZ2IiEhWphlaDDxWJ0sLT3R0NPbt22d+XlBQAL1e3+h0c0tMmzat3vUOHjyInj17MtQQEZHjqKgATI0BnJJudbIEnrFjx0Kr1WLTpk0AgOXLlyMuLg5KpRI6nQ41NTWtul5ERAQWLFiA/fv3Y+fOnUhKSsLcuXNtUToREZFtHD4MGI1AYCDQq5fc1TgdWbq0VCoV1q5di1mzZmHRokUwGAzIyMgAIMLLqlWrMGXKFIuvl5iYiDNnzmD8+PHo3r07Hn74YSQmJtqoeiIiIhtgd5ZNKSRLlzi2gaKiImRmZiI2NhbdunWTqwzodDr4+flBq9XC19dXtjqIiKgDmzED+PBDYOlSgP/TbpHWfH/L0sJjEhgYiMDAQDlLICIisg+coWVTnORPREQkt9JS4PRpcRwVJW8tToqBh4iISG6m8TsDBohVlsnqGHiIiIjkxu4sm2PgISIikhtnaNkcAw8REZGcJKn+HlpkEww8REREcjpzBigpAVxdgWHD5K7GaTHwEBERycnUujNsGODhIW8tToyBh4iISE6m8TvszrIpWRcedHobNwJPPSV3FdRWPXsC69YBo0fLXQm1Vm0tMGcOUFQEbNkCXOEGxSSjwkLg9tuBn3+WuxLb0WrFrxywbFMMPLZUVSX6ZcmxlZQACQmi2blPH7mrodZ4/HFg/XpxPH06sGuXGCdBjqG8HIiPB7Kz5a7E9jw8gOuvl7sKp8bAY0u33gpcfbXcVVBbGAzAPfcAWVnA5MnADz8APj5yV0WW+Ne/gLfeAhQKwNMT+PZb4NFHgbffFq+RfTMagTvuEGGne3fg00+de0G+gACgSxe5q3BqDDy25O/PJnRn8MUXom89JweYNQv47DNAqZS7KmrO7t2idQcAli8HBg8WrXRr14pj08/Ifj37LJCaCri7i//mrrpK7orIwXHQMlFLgoKAzz8XTc47dwJPPy13RdScvDzRfWU0AvfeCyxaJFrnkpPFz594AkhLk7dGat7GjcCKFeJ4/XqGHbIKBh4iS0RHA//+tzheubJuXAjZl9JSEW50OuCaa4A1a+q6rxYsAB54QASh224TLXZkf/bsAR56SBwvXiwGLBNZAQMPkaVuvRV44QVxPGcO8N13spZDf1FdDdxyC3DqFBASIsZ8uLvX/VyhAN58E7j2WqCsTAyGvXBBvnqpoVOngP/7P6CmBpg2re6/NyIrYOAhao2kJGDGDDHd+ZZbgPx8uSsiQCzNP2cO8P33gK+v6Hrs2rXh+9zcgE8+AcLCgIICYOpUQK9v93KpEVqtCKEXLwKRkaJF1YVfUWQ9/NtE1BoKBfDee2JNnt9/F/9A//GH3FXRypXAhg3iC/LDD8XA5KZ06SIGovv5Af/7n+g+kaT2q5Uaqq0V/yORmwv06iUGK3fqJHdV5GQYeIhay9NTzBoJDhaLod16q/gHm+Tx+ed1C3yuWgXccEPL5wwcCHz8sZhtt2lT3QBZkseTT4o1kjw9xZ9nYKDcFZETYuAhuhI9e4p/mL28gK+/5jRnuWRni6UCTF1ajz5q+bnjxwOrV4vjxERgxw7b1EjNe/vtuj+HTZtEdxaRDTDwEF2p4cPFdgUKhVjg7s035a6oYykuFl2KFRVihdrVq1u/oODcuXUh6Y47gJ9+sn6d1LT//rfu9//ll8VAZSIbYeAhaoubbxYL2wGilWf3bnnr6SiqqsRsnsJC4G9/E91TV7plxGuvARMmAJWVYnHCX3+1bq3UuOPHxXpJBoMIm88+K3dF5OQYeIjaatEisf2EwSD+Ac/Lk7si5yZJwH33Afv2AZ07ixlZnTtf+fVUKjHQeeBA4Nw5EWIvXbJevdTQ5QP+r7pKbM7L7T7Ixhh4iNpKoRDjEK65Rix4N3mymFpLtvHPfwIffCCCyiefAP37t/2aarUITl26AAcPigDLmVu2YVpj5+RJoHdvMXbKw0PuqqgDYOAhsgZ3d7HQXUiIWDxt6lSxEB5Z18cfi7WQADFu6u9/t961Q0PFn6GrK/DRR1z0zhYkSYzZSU8HvL1FyOzRQ+6qqINg4CGylq5dxT/gvr5iAbyHH2YrgTVlZgJ33y2OFywAHnzQ+p8xdqxorQNE4Nm2zfqf0ZG9/rrYwFWhEK10Q4fKXRF1IAw8RNY0eLAYD+LiIhYoXLlS7oqcw7lzYkDxpUvATTcBr75qu8+67z5g4UJxfM89wP79tvusjuQ//xHr7QBiI9fJk+WthzocBh4ia7vhBjHzBxAL4n3xhbz1OLqKCjGQ+NdfgSFDRMuAUmnbz1y+XAQsvV58dmGhbT/P2eXkiJWUjUbg/vtFCx1RO2PgIbKFefPEQniSJBbGO3JE7oock9EI3HUXcPgw0K2bCI++vrb/XKVSrLEUEQH89puYUVRebvvPdUYXLojfv7IysXHrW29xRhbJgoGHyBYUCrEQ3vXXiy/K+HjxxUmtk5QkBhK7uYnZPH37tt9ne3uLgNW9u1jR+Y47RAAjy+n1YgB/QYEYFP7JJ+LPkkgGDDxEtuLqKmYV/e1vwNmzwJQpYsE8sszmzcDSpeL43XeBMWPav4bevcVGlu7u4tfExPavwVFJkhhY/r//iY1aTdP+iWTCwENkS5cvjLdvnxi/wJlbLfvhB+CBB8RxYiJw553y1RITIwagA8ArrwAbN8pXiyNZsUKEVqVSBP+BA+WuiDo4Bh4iW+vfXzTlq1TA1q1i4TxqWkGBaA2rrhbdIS+/LHdFYhyWaf2fhx4C9uyRtx57t2NHXWvY6tVio1YimTHwELWHv/9dDNYExBfnxx/LW4+90unEeKeSEmDECLF7toud/DP1/PNi65CaGrGP16lTcldkn376SYx3AsQig3PnylsP0Z/s5F8Sog7gwQeB+fPF8d13i4X0qI7BIFpScnKAgADg888BLy+5q6rj4iK6s6KixNYh8fGAVit3Vfbl11/F70tlpdiQ1bQ8A5EdYOAhak/JyWLhvEuXxPouRUVyV2Q/nnoK+PJLsa9SaioQFCR3RQ116iRqCwwEcnPF2jK1tXJXZR8u/zs9cKBYgFOlkrsqIjMGHqL2pFSKhfOGDAHOnxeL21VUyF2V/N59F0hJEcf//jcQHS1vPc3p1Uu0Pnl6Art21a0e3JEZjWJV6oMHxUysnTvFhqxEdoSBh6i9+fqK9V26dRML6t11V8de3yU9Xew7Boj9q269Vd56LDFyJPD+++J49eq6/bc6qhdeEBuuurqKdZNCQ+WuiKgBBh4iOfTtK2ayuLmJL4glS+SuSB4nTwK33CK6hWbOrJsJ5QimTq2bcffoo8B//ytvPXL54APgxRfF8dtviw1YiewQAw+RXMaMEV05gPjiNLUYdBQajRjgqtEAo0cD69c73pYDpjWCDAYxg+v4cbkral/79wP33iuOFy4UG68S2SnZAk9OTg6io6PRuXNnLFq0CJKFi7Hl5+fD39+/wesZGRkYNGgQunbtihTTWAAie3fnnXXrldx/v1hwryOoqRFdV8ePA8HBwGefiTExjkahANatA2JjgT/+EAHu99/lrqp9nD0rBinr9eK+ly+XuyKiZskSePR6PeLj4xEZGYnMzEzk5uZiowWrl54+fRqTJk2CRqOp93pJSQkSEhIwc+ZM/Pjjj9iyZQvS09NtVD2Rlb38sljXpbpaLLhXUCB3RbYlScDjj4suIC8vMZ6pZ0+5q7py7u6ie7JPH9FFN22aCHTOrLxcDLj/7TexweqWLbbfwZ6orSQZ7NixQ+rcubNUUVEhSZIkZWVlSWPGjGnxvEGDBkmvvPKK9NeyX3vtNWnAgAGS0WiUJEmSPvvsM+n222+3uB6tVisBkLRabSvugsiKysslacQISQIkacgQSdLp5K7Idt54Q9ynQiFJqalyV2M9R45Ikre3uLcHH5SkP/89cjoGgyQlJIj77N5dkgoK5K6IOrDWfH/L0sKTnZ2NmJgYdOrUCQAQERGB3NzcFs/buXMnpk+f3uj1xo0bB8Wf/f+jRo3C4cOHm7yOXq+HTqer9yCSlZeXmOocECAW3ps5U4wLcTZffSVadwCx11JCgrz1WNPQocC2bWKBwnXrgNdfl7si20hMFH9XTRuq9ukjd0VEFpEl8Oh0OoSEhJifKxQKKJXKBl1Vf9WvXz+Lrufr64uiZhZ0W7ZsGfz8/MyP4ODgVt4BkQ0EBYkvEA8PsQDfU0/JXZF15eaKcTtGoxjounCh3BVZ36RJYnFJQKzP85//yFuPtW3cKDZQBcSGqjExspZD1BqyBB6VSgV3d/d6r3l4eKCystIq12vpWomJidBqteZHYWHhFX0ukdVFR4uF9wCxEJ9pFpejKy0VA1t1OuCaa8T0ZUebkWWp+fPFTu9Go1iJOSdH7oqs4/vvxcapALB4sdgGhMiByBJ4/P39UVJSUu+1srIyuLm5WeV6LV3L3d0dvr6+9R5EduPWW8VCboBYkO+772Qtp81Mu56fOgX06yfWHbrC/9YdgkIBvPkmcN11QFmZCHoXLshdVducOiX+DGtqxKBs099PIgciS+CJjo7Gvn37zM8LCgqg1+sbnW5+JdfLyspCYGBgm+skkk1SkhjHU1srFubLz5e7oisjScCcOcCePXUrTHftKndVtufmBmzfDoSFiVl3U6eK6duOSKsFJk8WG6ZGRYkWSHvZwZ6oFWT5Wzt27FhotVps2rQJALB8+XLExcVBqVRCp9OhppVTOhMSErB3716kp6ejtrYWycnJmDhxoi1KJ2ofCoVYiG/0aLGuy+TJYoE+R5OcDGzYIL4gP/wQGDxY7oraT5cuIuD5+QH/+x/w4IMiADqS2lrgttuAvDyxh1hqqthAlcgRtcOssUbt2LFD8vT0lLp37y516dJFysnJkSRJkvr06SPt2LGjyfNOnz7dYFq6JEnSm2++Kbm6ukpdu3aV+vTpIxUXF1tcC6elk9369VdJCg4WU4Dj4iSpulruiiyXmiqmngOStHq13NXI5+uvJUmpFL8Py5bJXU3rPPaYqNvTU5IyM+WuhqiB1nx/KyRJvv/lKCoqQmZmJmJjY9GtW7c2Xy8/Px95eXm49tprWzUuR6fTwc/PD1qtluN5yP5kZ4ttKCoqgLlzxfgQe3d5zQ8/LGp21kHKllizRvzZAWIM0//9n7z1WOLtt+s2df3kE9EtR2RnWvP9LWvgsRcMPGT3Pv9crMIsScAbb4jNKu1VcTEwahRQWAjExYmp2a6uclclv8ceE392nToBe/cCI0bIXVHT/vtf4IYbxFpQ//wn8OyzcldE1KjWfH9z5BmRI0hIqNur6PHHxQJ+9ujSJRHMCguBv/0N+Ogjhh2TlBRg4kSgslLM3Dp/Xu6KGnf8uNgI1WAA7rijbq83IgfHwEPkKBYtEgv2GY1i6npentwV1SdJYgPU/fuBzp2BnTvFrySoVGLg9qBBQFGR2HjzCtcesxnTAPk//hAboq5b17G7IsmpMPAQOQqFQoyruOYasYDf5MliQT978fLLwAcfiC/2Tz8F+veXuyL74+cnZm516QJkZtYFWHtgWmMnP19sF7Fjh1j1m8hJMPAQORI3NxEmQkLEYnC33CIW9pPbRx8BS5aI4zVrxKJ71LjQUPFn6Ooqft/sYRE/SQIeeQRITwe8vUUo695d7qqIrIqBh8jRdO0quot8fcVy/3PmyLu+y8GDwN13i+MFC8S2CtS8sWOBd94Rxy++KFrG5LRqVV331QcfiI1QiZwMAw+RIxo8WIwHcXERC/utXClPHefOibEoVVVi48xXX5WnDkd0771iXJbpeP9+eer48su6jVyTk0VXKZETYuAhclQ33CD+zxwQO6t//nn7fn5FhZg99uuvwJAhwNatgFLZvjU4umXLxO+hXi+C49mz7fv5OTliCxOjUbTMLVjQvp9P1I4YeIgc2aOPisXhJEnsXp2d3T6fazQCd90F/PQT0K2bGPPBNaxaT6kEtmwBIiKA334T4ae8vH0++8IF0ZpTVibGXHX0xSHJ6THwEDkyhQJ4/XXg+utFi0t8vFj4z9aSkup2Pd+xA+jb1/af6axMg4R79BCB9fbbbT9zS68Xqz2fOSM2ON2+3bl3sCcCAw+R43N1BT7+WCz0V1govsiqqmz3eZs3A0uXiuP168UWEtQ2vXsDn30GuLuLrklbLvYnSWIj0x9+qD9NnsjJMfAQOYPLF/rbtw+47z7bzNz63//qZmE9+6xYiZesIyZGDEAHgFdeATZutM3nLF8uQqtSKYLywIG2+RwiO8PAQ+Qs+vcXmzyqVGJq8csvW/f6BQWi9ai6Wmwk+dJL1r0+iQHESUni+KGHxLID1vTpp3X7Yq1eDYwfb93rE9kxBh4iZ/L3vwNvvSWOlywR/wdvDTqdGB9UUgKMHAls2iSmxJP1Pf+82MuqpkYEy1OnrHPdw4eBO+8Ux48+Wrd7O1EHwX+xiJzNgw/WTS+++26xhUFbGAxiBlhODhAQIMaYeHm1vU5qnIuL6M6KigIuXhQzqbTatl3z/HkxA6yyUmxg+tprVimVyJEw8BA5o1dfBW66SexenpAgFgi8UosWicXpPDxE2AkMtF6d1LhOnYDUVPF7nZcH3HYbUFt7ZdeqrBRr/BQViY1LP/xQdHsSdTAMPETOSKkU43iGDBELAyYkiGnrrbVuXV1rwKZNotWB2kevXiJgduoEfPUV8MQTrb+G0Qjcc49o5evSRczI8vOzeqlEjoCBh8hZ+fqKL7hu3cQCgXfd1br1XdLT68Z5vPiiGFdC7WvkSDGjCgDeeENszNoaL7wgxnG5uooBy6Gh1q+RyEEw8BA5s759xcKApl3WTTOAWnLypNiJvbZWjN9ZvNimZVIzpk6tW/do3jzgv/+17LwPPhBBFRAblY4da5v6iBwEAw+RsxszBnj3XXG8dGldi0FTNBoxUFajAUaPFosLcssBeT3zjJhhZTCIlrbjx5t//759YkNSQIzBMh0TdWAMPEQdwZ131q3e+8ADYpXdxtTUiC/UEyeA4GCx+q+HR7uVSU1QKMR4qthY4I8/RCD9/ffG33v2LDBlitg+IiFBbFBKRAw8RB3Gyy+L7pHqavGFWFBQ/+eSBDz+OPDNN2La+c6dQM+eclRKjXF3r9u3LD8fmDZNBNTLlZeLkPPbb2JD0i1buIM90Z8YeIg6ChcXMdNqxAixgGB8vFhQ0ORf/xKDYhUKYOtW8YVJ9qV7dzEQ3dtbDCp/5JG6LUQMBrHxaHZ2/fcREQAGHqKOxctLTHUOCBALCc6aJb4od+0C5s8X71mxQrQSkH0aMgTYtk0E2HXrgFWrxOuJieLP1t1drOHTu7esZRLZGwYeoo4mKEh8IXp4iAUF77pLLGxnNIrBrQsXyl0htWTSJCA5WRw/+SQwe7ZYbBIA3ntPbERKRPUoJMkWWyo7Fp1OBz8/P2i1Wvj6+spdDlH7+Phj4NZb656PHQt8/bWYwk72T5JE0Fm3ru61pKS6qehEHUBrvr/ZwkPUUU2fXvfl2K+f2GmdYcdxKBTAm2+KDWMB8ef5/POylkRkz9jCA7bwUAcmSWKKeng4oFbLXQ1dCb1e/Blecw33yKIOpzXf3/yvg6gjUyjEwoTkuNzd61p5iKhJ7NIiIiIip8fAQ0RERE6PgYeIiIicHgMPEREROT0GHiIiInJ6DDxERETk9Bh4iIiIyOkx8BAREZHTY+AhIiIip8fAQ0RERE5PtsCTk5OD6OhodO7cGYsWLYIlW3plZGRg0KBB6Nq1K1JSUur9LD4+HgqFwvyIi4uzVelERETkYGQJPHq9HvHx8YiMjERmZiZyc3OxcePGZs8pKSlBQkICZs6ciR9//BFbtmxBenq6+eeHDh3C0aNHodFooNFokJqaauO7ICIiIkchS+BJS0uDVqtFSkoKQkNDsXTpUqxfv77Zc7Zs2YKAgAAkJSWhf//+WLJkifmcc+fOQZIkDBkyBGq1Gmq1Gl5eXu1xK0REROQAZNktPTs7GzExMejUqRMAICIiArm5uS2eM27cOCgUCgDAqFGjkJiYCAA4cOAADAYDgoKCoNFoEB8fjzVr1qBz586NXkuv10Ov15ufa7VaAGKbeSIiInIMpu9tS4bFyBJ4dDodQkJCzM8VCgWUSiU0Gk2TIUWn02Hw4MHm576+vigqKgIAnDhxApGRkUhOToaLiwvuvfdePPvss1izZk2j11q2bBleeOGFBq8HBwe35baIiIhIBmVlZfDz82v2PQrJklhkZU8//TRqamrqDTwODg7Gvn37EBgY2Og5t912G8aMGYPHHnsMAGAwGODh4YGampoG783IyMC0adNQUlLS6LX+2sJjNBrx+++/o0uXLuYWJGvR6XQIDg5GYWEhfH19rXpte8D7c3zOfo+8P8fn7Pfo7PcH2O4eJUlCWVkZevXqBReX5kfpyNLC4+/vj5ycnHqvlZWVwc3NrdlzLg8wzb1frVajtLQUer0e7u7uDX7u7u7e4HW1Wt2KO2g9X19fp/2LDPD+nIGz3yPvz/E5+z06+/0BtrnHllp2TGQZtBwdHY19+/aZnxcUFECv18Pf39/ic7KyssytQdOmTav3s4MHD6Jnz56Nhh0iIiLqeGQJPGPHjoVWq8WmTZsAAMuXL0dcXByUSiV0Ol2j3VQJCQnYu3cv0tPTUVtbi+TkZEycOBGAGPS8YMEC7N+/Hzt37kRSUhLmzp3brvdERERE9kuWLi2VSoW1a9di1qxZWLRoEQwGAzIyMgCI8LJq1SpMmTKl3jldu3bFypUrMXHiRPj5+cHLy8s8LT0xMRFnzpzB+PHj0b17dzz88MPmGVxyc3d3xz/+8Q+nbW3i/Tk+Z79H3p/jc/Z7dPb7A+zjHmUZtGxSVFSEzMxMxMbGolu3bhadk5+fj7y8PFx77bVO39dJRERE1iFr4CEiIiJqD9w8lIiIiJweAw+1ycWLF/HDDz+gtLRU7lKIiIiaxMBjQ6mpqejXrx9UKhVGjx6NvLw8uUuyqm3btiEsLAyPPPIIevfujW3btsldks3ccMMNLW5w62jmzZsHhUJhfoSFhcldks0888wziI+Pl7sMq9u4cWO9P0PTw5n+rm7evBm9e/eGt7c34uLiUFBQIHdJVrVhwwbzPpAzZ87k/zzakkQ2kZ+fL3Xu3Fn68MMPpeLiYmn69OlSbGys3GVZjUajkbp27SodPXpUkiRJ2rRpk9S7d2+Zq7KN999/XwIgbdiwQe5SrOqqq66SvvzyS0mj0UgajUbS6XRyl2QTR48elXx8fKT8/Hy5S7E6vV5v/vPTaDRSYWGh1LVrV+mXX36RuzSryM/Pl4KDg6VDhw5JZ86cke677z7p2muvlbssq/n6668lb29vaffu3VJBQYF00003SVdffbXcZVlFaWmp1LdvX+n06dPm144ePSpFRUVJarVaWrhwoWQ0Gtu1JgYeG/niiy+kNWvWmJ9/++23kpubm4wVWdfZs2el999/3/w8Oztb8vHxkbEi27h48aLUo0cPacCAAU4VeGpqaiQfHx+prKxM7lJsymg0SrGxsVJSUpLcpbSLf/7zn9JDDz0kdxlW8/HHH0vTp083P9+zZ48UEBAgY0XWdeedd0rz5883Pz927JgEQCotLZWxqrYrKSmRYmJiJADmwFNVVSX17dtXmj17tpSfny/ddNNN0nvvvdeudbFLy0YmT56MOXPmmJ8fP37cqboMgoODcfvttwMAampqkJycjKlTp8pclfU9+eST+L//+z/ExMTIXYpVHTlyBJIkYfjw4fD09MQNN9yAs2fPyl2W1a1btw5ZWVkICQnBzp07G13U1FlUVVXh9ddft5s1yKxh8ODB+Pbbb/HTTz9Bq9XizTffxPjx4+Uuy2pKS0vRu3dv83OlUglArFXnyGbMmIEZM2bUey0tLQ1arRYpKSkIDQ3F0qVLzWvptRcGnnZQXV2N5ORkp1z9OTs7Gz169MDu3buxatUqucuxqvT0dHzzzTdYsWKF3KVYXV5eHsLDw/HBBx8gNzcXrq6umD17ttxlWVV5eTkWL16M/v3749y5c0hJScHYsWNRVVUld2k2sXXrVsTExKBv375yl2I1gwcPxrRp0zBy5Eio1Wrs378fycnJcpdlNcOHD8fnn38O6c/VYTZs2IBRo0ZZvDeUvVq7di0ef/zxeq9lZ2cjJiYGnTp1AiAWGc7NzW3Xuhh42sHixYvh7e2Nhx56SO5SrC4iIgLffPMNwsPDce+998pdjtVUVVVh9uzZWLNmjVMucHn77bdj3759iI6ORkhICP71r39h9+7d0Ol0cpdmNZ9++ikqKirw7bffIikpCbt378Yff/xh3tLG2bz99tv1WpWdwb59+/DFF19g//79KCsrw8yZM3HTTTeZA4KjW7hwIaqrqxEZGYnY2FisWLECjz76qNxltVm/fv0avKbT6RASEmJ+rlAooFQqodFo2q0uBh4b+/rrr/H2229j69atcHV1lbscq1MoFBgxYgQ2btyI1NTUdv3La0svvfQSoqOjMWnSJLlLaRdqtRpGoxG//vqr3KVYzblz5zB69GjzpsQqlQoRERE4ffq0zJVZX35+PvLz8xEXFyd3KVb14YcfYsaMGRg1ahS8vb3x8ssv49SpU8jOzpa7NKvw9/fH//73P3z00UeIiIjAwIEDMWvWLLnLsgmVStVgWwkPDw9UVla2Ww0MPDZ06tQp3H777VizZg0GDx4sdzlW9e2332LRokXm56Y+ZxcX5/grtXXrVqSmpkKtVkOtVmPr1q2YO3eu03RLPvHEE/joo4/Mzw8ePAgXFxcEBwfLWJV1BQcH49KlS/VeO3PmDPr06SNTRbbz0UcfYfLkyU73P1W1tbX47bffzM/LyspQUVEBg8EgY1XW16tXL3z66adYtmyZeRyPs/H390dJSUm918rKyuDm5tZuNTj2yCg7dunSJUyePBlTpkzBzTffjPLycgCAl5cXFAqFzNW13cCBAzFlyhT0798fN954IxYvXowJEyY4fN+zyZ49e1BbW2t+vnDhQsTExOCee+6RrygrGj58OJ577jn07NkTtbW1mDdvHu655x5z/7ozmDRpEubNm4e3334bkydPxqeffoqsrCzccMMNcpdmdbt27XKqLmWTMWPG4L777sNrr72GHj164N1330WPHj0QEREhd2lW9cYbb5j/TXVW0dHRePfdd83PCwoKoNfrzS2w7aJd54R1IDt27JAANHhcviaBo9u1a5c0aNAgycfHR5o2bZp04cIFuUuymbvvvtuppqVLkiQ988wzklqtloKDg6XHHntMKi8vl7skq/vxxx+l2NhYydPTUwoJCZF27Nghd0lWV1lZKbm5uUl5eXlyl2J1RqNRev7556XevXtLrq6u0ogRI6TMzEy5y7IqjUYj+fv7SwcOHJC7FKu7/DuvpqZG6tatm/Tvf/9bkiRJmj17tjR58uR2rYebhxIREZHVKRQKnD592jxz8LPPPsOsWbPg4+MDg8GAjIwMhIeHt189DDxERETUHoqKipCZmYnY2Fh069atXT+bgYeIiIicnnNMqSEiIiJqBgMPEREROT0GHiIiInJ6DDxE5DC++OILFBUV1XstNTUVJ06caPBeo9GI6urqBq/X1NTAaDRa9Hm///57vefV1dXmNbWIyLEw8BCRw5g3b16DDQcXL16M9PT0Bu89cuQIQkND4e7ujiFDhmDIkCHw9fVFcHAwDh061OLO6VVVVQgNDUVqaqr5te+//x7du3dvsIIzEdk/Bh4isgsFBQXNrkJeXl6OsrIyjBs3zvzamTNncOrUKcTGxpr3kyouLgYgVpPes2cPgoODkZOTg5ycHIwbNw4bN27Ef//7X3h6eqJv377o27cvvLy88Pbbb9f7vM8//xzdu3eHRqNBSEgIwsLCcPfdd6OmpgZDhw5FWFgY+vbtizfffNM2vyFEZFXcWoKI7N5dd92FnTt3oqysDD169AAAfPfdd/j888/h5eWF6dOnAxB784wZM6bePmGNmTdvHpKTk1FQUAAAuP766+vtIyZJEl5++WXMmzcPd9xxB+68804olUps2rQJa9aswY8//ghAdI85w1YxRB0BW3iIyO5VVVUhJSUFNTU1KC0thbe3NyRJwoYNG5Camoqff/4ZP//8M1asWNFgR+bCwkJzl9a3334LAPD29salS5fM3Vr5+fkICQkxn/Pee+/h6NGj6NmzJ1QqFZYsWYKoqCgsXrwYubm5iIqKQlRUFFJTU80b5xKRfWPgISK7YzAYMHnyZPNmrS4uDf+pevfdd9GtWzdcddVV5tdqamrg4eFR731/7dIy6dWrF4qLi/H777+jtLQUAwYMACC6yRYuXGhuSQJEaHrggQdw9uxZaLVaZGZmIioqCjqdzpq3TUQ2xP81ISK7M2/ePBgMhnq7K/9VQkICpkyZgvvuuw9vvfUWPDw8UFNT06CFpymDBg3CsWPHUFFRgVGjRkGpVAIAPvnkE0yYMAF6vd78XhcXFyxZsgTJycnm10pKShATE3OFd0hE7Y0tPERkV1599VWsWbMGb7/9dr3uonnz5qFr167o2rUrCgsLERgYiOuuuw5ZWVnYuHEjANH19dfAc+bMGfPg5N27d5tfHzduHNLS0vDee+9h2rRp5tcXLFjQaNB68cUXzQOj8/PzMXPmTCvfORHZEgMPEdmVd955B7feeitWrVpV7/U33ngDpaWlKC0tNQ8wVigUWLBgAV577TVIkoSqqip4enrWO69Pnz4oKChAQUEBJkyYYH799ttvx4cffoiDBw/irrvuMr+uUCjg4+PToK6XXnrJPBZoyJAhLQ6MJiL7wi4tIrIru3btgoeHB8LDw/H444+jb9++zb5/2rRp+Prrr1FRUYHKykr4+fk1eE9FRQUOHz6MwsJCPPPMM/jyyy+RnJwMb29vBAQEwNvbu8W6kpKSMGfOHPPzy4+JyP6xhYeI7EpYWBiCgoJwzz33YPHixS2+39PTE5s2bYK3tzc0Gg18fX0BAGfPnsXChQtRUFCAq6++Gp9++imMRiOee+45rF69Gg8++CDCw8Ph6+uL+++/v8XFBNnCQ+TYGHiIyC4lJiYiNTUVWVlZqK2tbTCGp7a2tt77q6qqsH//fvTv3x8A0LNnT4wcORL5+fn46aef8Nprr5m7wm688UZkZ2dj06ZN+Oijj3Dq1CkMHz4cJSUl5usZDAZUV1ebp64nJSWZZ3vl5OTg1ltvBYB6g5uJyH4pJEmS5C6CiKg5KSkpiI6OxjXXXAMAWLFiBe6880706tULly5dQlxcHLy9vTFp0iTMmzevycUAJ06ciEceeQQ1NTUYN24cOnfuDEBMZ09PT683xicuLg5jxozBunXr4Orq2ug1JUmCq6sr8vPzbXDXRGRNDDxERETk9NilRURERE6PgYeIiIicHgMPEREROT0GHiIiInJ6DDxERETk9Bh4iIiIyOkx8BAREZHTY+AhIiIip8fAQ0RERE6PgYeIiIic3v8DpV3thdcSwHcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "x=np.array([[19,30],[30,40],[39,47],[40,52],[47,50],[50,55],[60,60],[62,65],[73,70],[75,82],[77,85],[90,95],[92,90]])\n",
    "y=np.array([0,0,0,0,0,0,1,1,1,1,1,1,1])\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)\n",
    "k_range=range(2,11)\n",
    "k_error=[]\n",
    "for k in k_range:\n",
    "    model=KNeighborsClassifier(n_neighbors=k)\n",
    "    scores=cross_val_score(model,x,y,cv=5,scoring='accuracy')\n",
    "    k_error.append(1-scores.mean())\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.plot(k_range,k_error,'r-')\n",
    "plt.xlabel('k的取值')\n",
    "plt.ylabel('预测误差值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da9230ea-3a95-4437-b21b-26555d22fd86",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "k=5时，预测样本的分类结果为 [0]\n",
      "k=7时，预测样本的分类结果为 [0]\n"
     ]
    }
   ],
   "source": [
    "model1=KNeighborsClassifier(5)\n",
    "model1.fit(x_train,y_train)\n",
    "model2=KNeighborsClassifier(7)\n",
    "model2.fit(x_train,y_train)\n",
    "pred1=model1.predict([[55,65]])\n",
    "pred2=model2.predict([[55,65]])\n",
    "print(\"k=5时，预测样本的分类结果为\",pred1)\n",
    "print(\"k=7时，预测样本的分类结果为\",pred2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "5d54a901-8e1e-4669-8b2c-76240290a816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from sklearn.neighbors import KNeighborsRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "x=np.array([[182],[178],[170],[168],[165],[162],[158],[154],[149],[144]])\n",
    "y=np.array([[113],[105],[86],[83],[86],[74],[72],[45],[49],[43]])\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.3,random_state=0)\n",
    "k_range=range(2,8)\n",
    "k_error=[]\n",
    "for k in k_range:\n",
    "    model=KNeighborsRegressor(n_neighbors=k)\n",
    "    model.fit(x_train,y_train)\n",
    "    scores=model.score(x_test,y_test)\n",
    "    k_error.append(1-scores)\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.plot(k_range,k_error,'r-')\n",
    "plt.xlabel('k的取值')\n",
    "plt.ylabel('预测误差值')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "7644e019-e9d3-42d6-be36-bd3ad9003e57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model=KNeighborsRegressor(3)\n",
    "model.fit(x_train,y_train)\n",
    "plt.xlabel('身高/cm')\n",
    "plt.ylabel('体重/kg')\n",
    "plt.rcParams['font.sans-serif']='Simhei'\n",
    "plt.axis([140,190,40,140])\n",
    "plt.scatter(x,y,s=60,c='k',marker='o')\n",
    "plt.plot(x,model.predict(x),'r-')\n",
    "plt.show()"
   ]
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